宫颈癌是一种影响妇女的普遍和令人担忧的疾病,随着发病率和死亡率的增加。早期发现在改善结果方面起着至关重要的作用。计算机视觉的最新进展,尤其是Swin变压器,在图像分类任务中表现出了有希望的性能,与传统卷积神经网络(CNN)相媲美或超越传统卷积神经网络。Swin变压器采用分层和有效的方法,使用移位窗口,支持捕获图像中的本地和全局上下文信息。在本文中,我们提出了一种名为Swin-GA-RF的新方法,以增强子宫颈涂片图像中宫颈细胞的分类性能.Swin-GA-RF结合了Swin变压器的优势,遗传算法(GA)特征选择,以及用随机森林分类器替换softmax层。我们的方法涉及从Swin变换器中提取特征表示,利用遗传算法来识别最佳特征集,并采用随机森林作为分类模型。此外,数据增强技术用于增强SIPaKMeD1宫颈癌图像数据集的多样性和数量。我们使用两类和五类宫颈癌分类比较了Swin-GA-RF变压器与预训练的CNN模型的性能,同时使用Adam和SGD优化器。实验结果表明,Swin-GA-RF优于其他Swin变换器和预训练的CNN模型。使用Adam优化器时,Swin-GA-RF在二进制和五类分类任务中实现了最高的性能。具体来说,对于二元分类,它达到了准确性,精度,召回,F1评分分别为99.012、99.015、99.012和99.011。在五类分类中,它达到了准确性,精度,召回,F1评分分别为98.808、98.812、98.808和98.808。这些结果强调了Swin-GA-RF方法在宫颈癌分类中的有效性,证明其作为早期诊断和筛查计划的宝贵工具的潜力。
Cervical cancer is a prevalent and concerning disease affecting women, with increasing incidence and mortality rates. Early detection plays a crucial role in improving outcomes. Recent advancements in computer vision, particularly the Swin transformer, have shown promising performance in image classification tasks, rivaling or surpassing traditional convolutional neural networks (CNNs). The Swin transformer adopts a hierarchical and efficient approach using shifted windows, enabling the capture of both local and global contextual information in images. In this paper, we propose a novel approach called Swin-GA-RF to enhance the classification performance of cervical cells in Pap smear images. Swin-GA-RF combines the strengths of the Swin transformer, genetic algorithm (GA) feature selection, and the replacement of the softmax layer with a random forest classifier. Our methodology involves extracting feature representations from the Swin transformer, utilizing GA to identify the optimal feature set, and employing random forest as the classification model. Additionally, data augmentation techniques are applied to augment the diversity and quantity of the SIPaKMeD1 cervical cancer image dataset. We compare the performance of the Swin-GA-RF Transformer with pre-trained CNN models using two classes and five classes of cervical cancer classification, employing both Adam and SGD optimizers. The experimental results demonstrate that Swin-GA-RF outperforms other Swin transformers and pre-trained CNN models. When utilizing the Adam optimizer, Swin-GA-RF achieves the highest performance in both binary and five-class classification tasks. Specifically, for binary classification, it achieves an accuracy, precision, recall, and F1-score of 99.012, 99.015, 99.012, and 99.011, respectively. In the five-class classification, it achieves an accuracy, precision, recall, and F1-score of 98.808, 98.812, 98.808, and 98.808, respectively. These results underscore the effectiveness of the Swin-GA-RF approach in cervical cancer classification, demonstrating its potential as a valuable tool for early diagnosis and screening programs.